R IESGO DE M ERCADO

The resulting distributions of the total annual space heating energy consumption and associated carbon dioxide emissions of the Belgrade housing stock in the year 2010 are presented in Figures 6.2 and 6.3, respectively, and their descriptive statistics are summarised in Table 6.6. The mean annual space heating energy consumption of the Belgrade housing stock is 5,265GWh, which is very close to the prediction of the BEDEM model (5,242GWh) based on the mean values of the input parameters (see Table 6.2). Similarly, the discrepancy between the mean carbon dioxide emissions associated with the total domestic space heating energy consumption (1,690,090tCO2) and the prediction of the BEDEM model (1,640,865tCO2) is rather small. This is in accordance with the central limit theorem which states that the mean value of all input parameters will determine the mean value of the results. The distributions’ skewness to the right may be explained by the lognormal distribution of the majority of input parameters (see Chapter 3). The standard deviations of the total space heating energy consumption and carbon dioxide emissions are 600GWh and 227,606tCO2, respectively. While 90% of the space heating energy use predictions fell within a range of 1,960GWh around the mean (±19% of the mean), 50% of the predictions were within a range of 847GWh around the mean (±8% of the mean). In addition, 90% of the carbon dioxide emissions predictions fell within a range of 765,035tCO2 around the mean (±23% of the mean), and 50% of the predictions were within a range of 313,898tCO2 (± 9% of the mean).

The results presented herein indicate that uncertainty in the BEDEM model predictions is rather large. This uncertainty is born of a lack of knowledge of certain input parameters which have a large impact on the total domestic space heating energy consumption and associated carbon dioxide emissions. Consequently, in order to reduce uncertainty in predictions of the domestic energy models a detailed and comprehensive testing and monitoring programme should be conducted across the Belgrade’s residential built environment. The input parameters which cause the greatest uncertainty in the BEDEM model predictions in the base case year are presented in Figure 6.4.

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Figure 6.2 Distribution of the Belgrade housing stock space heating energy consumption in the

year 2010

Figure 6.3 Distribution of the Belgrade housing stock carbon dioxide emissions of space

heating energy consumption in the year 2010

4364 6324 5.0% 90.0% 5.0% 0 1 2 3 4 5 6 7 4000 4500 5000 5500 6000 6500 7000 7500 Fre q u en cy (% )

Space heating energy consumption (GWh)

1.348 2.113 5.0% 90.0% 5.0% 0 0.5 1 1.5 2 2.5 1 .2 ₁.4 ₁.6 ₁.8 2 ₂.2 ₂.4 ₂.6 ₂.8 Fre q u en cy (% )

Space heating energy consumption CO2 emissions (mil. tCO2) 5265

1.690 ,09 0

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Table 6.6 Descriptive statistics of the resulting distribution in the year 2010

Figure 6.4 illustrates the results of the sensitivity analysis which are expressed as the amounts of change in the Belgrade domestic space heating energy consumption due to a one standard deviation increase in the nine most important input parameters. SFH generate the greatest uncertainty in the BEDEM model, as one standard deviation increase in the space heating system energy use of SFH leads to a 527GWh increase in the total housing stock space heating energy consumption, which is equivalent to around 10% of the mean value (5,265GWh). This is followed by the SFH space heating system seasonal efficiency, as one standard deviation increase in its efficiency generates reductions of 189GWh (~3.6% of the mean) in the space heating energy consumption of the Belgrade housing stock. The third most influential parameter are MSB 1971/1980, as one standard deviation increase in their space heating energy use results in a 129GWh (~2.5% of the mean) increase in the overall domestic space heating energy consumption. The rest of the input parameters have considerably less impact on the variation in output variable, ranging from 84GWh to 8GWh. The largest impact of SFH on the BEDEM model predictions may be explained with a much larger uncertainty related to SFH compared to other important input parameters, and hence more widely spread-out distributions of their space heating system energy consumption and efficiency (see Chapter 3), in conjunction with their large share (~50%) within Belgrade’s housing stock. Therefore, SFH should in particular be subjected to detailed and extensive monitoring projects.

Q heat 2010 (GWh) CO2 emissions 2010 (t) Mean 5,265 1,690,090 Median 5,224 1,660,422 Standard Deviation 600 227,606 Coefficient of variation (%) 11 14 Interquartile range 847 313,898 Range 3,086 1,465,254 Minimum 7,156 1,234,481 Maximum 4,070 2,699,735 5th _{Percentile 4,364 1,348,035} 25th Percentile 4,813 1,525,813 75th Percentile 5,660 1,839,711 95th Percentile 6,324 2,113,070

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Figure 6.4 Change in space heating energy consumption due to a +1 SD change in input

6.6 Summary

The BEDEM model predictions are comparable to both the official top-down and measured data. The results of the local sensitivity analysis suggest that mean indoor temperature, efficiency of space heating system, external air temperature, and window U-value almost exclusively influence space heating energy consumption and, therefore, are the most influential factors of dwelling energy use and carbon dioxide emissions. While it is very likely that the large error/uncertainty of these input parameters will lead to inaccurate predictions in the model, knowing only these inputs is not a sufficient condition for obtaining accurate results. However, adding accurate values for the factors, such as the U-values of wall, roof and floor, air tightness, and building geometry, considerably increases the probability of obtaining reliable predictions. Furthermore, all the parameters with high sensitivities (listed above) have 30% to 70% more influence on the carbon dioxide emissions of SFH than on the emissions from post-1981 MSB. Hence, it is very likely that measures targeted at SFH and older MSB will have a larger effect than in new dwellings. Nevertheless, SFH are also more susceptible to the underperformance of almost all input parameters under study than any of the other building categories because of their greater exposed envelope area and higher heating demands. In this regard, the attention of policymakers and researchers ought to be focused on buildings for which sensitivities are greatest, whilst the attention of builders and those undertaking improvement measures ought to be focused on quality control if desired carbon reduction targets are to be met.

527 -189 129 84 68 54 -49 8 8 -300 -200 -100 0 100 200 300 400 500 600

SFH space heating energy use SFH space heating system efficiency MSB 1971/80 space heating energy use MSB 1946/70 space heating energy use MSB 1981/97 space heating energy use MSB 1998/10 space heating energy use DH system efficiency New space heating energy use Demolished space heating energy use

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Tests have shown that the principle of linearity holds within a modest range of input change ( but not over the practical range of some input parameters, such as insulation thickness and space heating system efficiency. In addition, if the effects of input uncertainties are linear, then the impact of cumulative changes of these parameters can be estimated from the sum of their individual effects. These findings indicate the possibility of making rapid estimates of the effects of different energy-efficient measures on carbon dioxide emissions in dwellings and, conversely, for assessing the effects of the underperformance of multiple refurbishment interventions. Although such simple models would provide rough estimates of carbon emissions, they may be sufficient to allow policymakers to distinguish between two or more scenarios or energy efficiency strategies.

The results of the MC analysis show that the uncertainty in the BEDEM model’s predictions is rather large, as 90% of the space heating energy use predictions fell within ±18.5% of the mean, and 50% of the predictions were within ±8% of the mean. Similarly, 90% of the carbon dioxide emissions predictions fell within ±23% of the mean, and 50% of the predictions were within ± 9% of the mean. These large uncertainties are due to the lack of knowledge of input parameters related to SFH which, for their large share (~50%), significantly affect the total space heating energy consumption of Belgrade’s housing stock. Therefore, there is a real need for detailed and extensive monitoring projects across Belgrade’s housing stock, and in particular SFH, in order to reduce uncertainty in the prediction of the domestic energy models. However, it should be noted that carrying out a field survey is not an easy task due to the considerable barriers and constrains, including: cost of monitoring campaign; recruitment, ongoing contact, and retention of households; transmission and storage of large data; and instalment and battery change of data logger devices.

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